RE practices are not well established for ML-enabled systems. Some secondary studies have recently given attention to this [1]}[2]}. For instance, while there is still limited research, the SE community agrees on the need of extending RE techniques for ML. Furthermore, the new proposed techniques have not yet been applied in practice [3]}. On the other hand, there is a misconception about the new role of data scientists that often are taking of responsibilities such as defining and eliciting requirements by themselves [4]}. Another particular challenge is the overconfidence of using ML [5]}. Customers see ML as magic, in other words, “ML will solve everything”. It's the job of RE to let stakeholders know the limitations and manage their expectations.
